/pandas/tests/test_multilevel.py
Python | 2055 lines | 1908 code | 113 blank | 34 comment | 8 complexity | 652a9e533a6a899bdb5497d6302a19e9 MD5 | raw file
Possible License(s): BSD-3-Clause, Apache-2.0
- # -*- coding: utf-8 -*-
- # pylint: disable-msg=W0612,E1101,W0141
- import datetime
- import itertools
- from warnings import catch_warnings, simplefilter
- import numpy as np
- from numpy.random import randn
- import pytest
- import pytz
- from pandas.compat import (
- StringIO, lrange, lzip, product as cart_product, range, u, zip)
- from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype
- import pandas as pd
- from pandas import DataFrame, Series, Timestamp, isna
- from pandas.core.index import Index, MultiIndex
- import pandas.util.testing as tm
- AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew', 'mad',
- 'std', 'var', 'sem']
- class Base(object):
- def setup_method(self, method):
- index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
- 'three']],
- codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
- [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
- names=['first', 'second'])
- self.frame = DataFrame(np.random.randn(10, 3), index=index,
- columns=Index(['A', 'B', 'C'], name='exp'))
- self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
- codes=[[0, 1, 2, 3]], names=['first'])
- # create test series object
- arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
- ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
- tuples = lzip(*arrays)
- index = MultiIndex.from_tuples(tuples)
- s = Series(randn(8), index=index)
- s[3] = np.NaN
- self.series = s
- self.tdf = tm.makeTimeDataFrame(100)
- self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
- lambda x: x.day]).sum()
- # use Int64Index, to make sure things work
- self.ymd.index.set_levels([lev.astype('i8')
- for lev in self.ymd.index.levels],
- inplace=True)
- self.ymd.index.set_names(['year', 'month', 'day'], inplace=True)
- class TestMultiLevel(Base):
- def test_append(self):
- a, b = self.frame[:5], self.frame[5:]
- result = a.append(b)
- tm.assert_frame_equal(result, self.frame)
- result = a['A'].append(b['A'])
- tm.assert_series_equal(result, self.frame['A'])
- def test_append_index(self):
- idx1 = Index([1.1, 1.2, 1.3])
- idx2 = pd.date_range('2011-01-01', freq='D', periods=3,
- tz='Asia/Tokyo')
- idx3 = Index(['A', 'B', 'C'])
- midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
- midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
- result = idx1.append(midx_lv2)
- # see gh-7112
- tz = pytz.timezone('Asia/Tokyo')
- expected_tuples = [(1.1, tz.localize(datetime.datetime(2011, 1, 1))),
- (1.2, tz.localize(datetime.datetime(2011, 1, 2))),
- (1.3, tz.localize(datetime.datetime(2011, 1, 3)))]
- expected = Index([1.1, 1.2, 1.3] + expected_tuples)
- tm.assert_index_equal(result, expected)
- result = midx_lv2.append(idx1)
- expected = Index(expected_tuples + [1.1, 1.2, 1.3])
- tm.assert_index_equal(result, expected)
- result = midx_lv2.append(midx_lv2)
- expected = MultiIndex.from_arrays([idx1.append(idx1),
- idx2.append(idx2)])
- tm.assert_index_equal(result, expected)
- result = midx_lv2.append(midx_lv3)
- tm.assert_index_equal(result, expected)
- result = midx_lv3.append(midx_lv2)
- expected = Index._simple_new(
- np.array([(1.1, tz.localize(datetime.datetime(2011, 1, 1)), 'A'),
- (1.2, tz.localize(datetime.datetime(2011, 1, 2)), 'B'),
- (1.3, tz.localize(datetime.datetime(2011, 1, 3)), 'C')] +
- expected_tuples), None)
- tm.assert_index_equal(result, expected)
- def test_dataframe_constructor(self):
- multi = DataFrame(np.random.randn(4, 4),
- index=[np.array(['a', 'a', 'b', 'b']),
- np.array(['x', 'y', 'x', 'y'])])
- assert isinstance(multi.index, MultiIndex)
- assert not isinstance(multi.columns, MultiIndex)
- multi = DataFrame(np.random.randn(4, 4),
- columns=[['a', 'a', 'b', 'b'],
- ['x', 'y', 'x', 'y']])
- assert isinstance(multi.columns, MultiIndex)
- def test_series_constructor(self):
- multi = Series(1., index=[np.array(['a', 'a', 'b', 'b']), np.array(
- ['x', 'y', 'x', 'y'])])
- assert isinstance(multi.index, MultiIndex)
- multi = Series(1., index=[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']])
- assert isinstance(multi.index, MultiIndex)
- multi = Series(lrange(4), index=[['a', 'a', 'b', 'b'],
- ['x', 'y', 'x', 'y']])
- assert isinstance(multi.index, MultiIndex)
- def test_reindex_level(self):
- # axis=0
- month_sums = self.ymd.sum(level='month')
- result = month_sums.reindex(self.ymd.index, level=1)
- expected = self.ymd.groupby(level='month').transform(np.sum)
- tm.assert_frame_equal(result, expected)
- # Series
- result = month_sums['A'].reindex(self.ymd.index, level=1)
- expected = self.ymd['A'].groupby(level='month').transform(np.sum)
- tm.assert_series_equal(result, expected, check_names=False)
- # axis=1
- month_sums = self.ymd.T.sum(axis=1, level='month')
- result = month_sums.reindex(columns=self.ymd.index, level=1)
- expected = self.ymd.groupby(level='month').transform(np.sum).T
- tm.assert_frame_equal(result, expected)
- def test_binops_level(self):
- def _check_op(opname):
- op = getattr(DataFrame, opname)
- month_sums = self.ymd.sum(level='month')
- result = op(self.ymd, month_sums, level='month')
- broadcasted = self.ymd.groupby(level='month').transform(np.sum)
- expected = op(self.ymd, broadcasted)
- tm.assert_frame_equal(result, expected)
- # Series
- op = getattr(Series, opname)
- result = op(self.ymd['A'], month_sums['A'], level='month')
- broadcasted = self.ymd['A'].groupby(level='month').transform(
- np.sum)
- expected = op(self.ymd['A'], broadcasted)
- expected.name = 'A'
- tm.assert_series_equal(result, expected)
- _check_op('sub')
- _check_op('add')
- _check_op('mul')
- _check_op('div')
- def test_pickle(self):
- def _test_roundtrip(frame):
- unpickled = tm.round_trip_pickle(frame)
- tm.assert_frame_equal(frame, unpickled)
- _test_roundtrip(self.frame)
- _test_roundtrip(self.frame.T)
- _test_roundtrip(self.ymd)
- _test_roundtrip(self.ymd.T)
- def test_reindex(self):
- expected = self.frame.iloc[[0, 3]]
- reindexed = self.frame.loc[[('foo', 'one'), ('bar', 'one')]]
- tm.assert_frame_equal(reindexed, expected)
- with catch_warnings(record=True):
- simplefilter("ignore", DeprecationWarning)
- reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
- tm.assert_frame_equal(reindexed, expected)
- def test_reindex_preserve_levels(self):
- new_index = self.ymd.index[::10]
- chunk = self.ymd.reindex(new_index)
- assert chunk.index is new_index
- chunk = self.ymd.loc[new_index]
- assert chunk.index is new_index
- with catch_warnings(record=True):
- simplefilter("ignore", DeprecationWarning)
- chunk = self.ymd.ix[new_index]
- assert chunk.index is new_index
- ymdT = self.ymd.T
- chunk = ymdT.reindex(columns=new_index)
- assert chunk.columns is new_index
- chunk = ymdT.loc[:, new_index]
- assert chunk.columns is new_index
- def test_repr_to_string(self):
- repr(self.frame)
- repr(self.ymd)
- repr(self.frame.T)
- repr(self.ymd.T)
- buf = StringIO()
- self.frame.to_string(buf=buf)
- self.ymd.to_string(buf=buf)
- self.frame.T.to_string(buf=buf)
- self.ymd.T.to_string(buf=buf)
- def test_repr_name_coincide(self):
- index = MultiIndex.from_tuples([('a', 0, 'foo'), ('b', 1, 'bar')],
- names=['a', 'b', 'c'])
- df = DataFrame({'value': [0, 1]}, index=index)
- lines = repr(df).split('\n')
- assert lines[2].startswith('a 0 foo')
- def test_delevel_infer_dtype(self):
- tuples = [tuple
- for tuple in cart_product(
- ['foo', 'bar'], [10, 20], [1.0, 1.1])]
- index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
- df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
- index=index)
- deleveled = df.reset_index()
- assert is_integer_dtype(deleveled['prm1'])
- assert is_float_dtype(deleveled['prm2'])
- def test_reset_index_with_drop(self):
- deleveled = self.ymd.reset_index(drop=True)
- assert len(deleveled.columns) == len(self.ymd.columns)
- assert deleveled.index.name == self.ymd.index.name
- deleveled = self.series.reset_index()
- assert isinstance(deleveled, DataFrame)
- assert len(deleveled.columns) == len(self.series.index.levels) + 1
- assert deleveled.index.name == self.series.index.name
- deleveled = self.series.reset_index(drop=True)
- assert isinstance(deleveled, Series)
- assert deleveled.index.name == self.series.index.name
- def test_count_level(self):
- def _check_counts(frame, axis=0):
- index = frame._get_axis(axis)
- for i in range(index.nlevels):
- result = frame.count(axis=axis, level=i)
- expected = frame.groupby(axis=axis, level=i).count()
- expected = expected.reindex_like(result).astype('i8')
- tm.assert_frame_equal(result, expected)
- self.frame.iloc[1, [1, 2]] = np.nan
- self.frame.iloc[7, [0, 1]] = np.nan
- self.ymd.iloc[1, [1, 2]] = np.nan
- self.ymd.iloc[7, [0, 1]] = np.nan
- _check_counts(self.frame)
- _check_counts(self.ymd)
- _check_counts(self.frame.T, axis=1)
- _check_counts(self.ymd.T, axis=1)
- # can't call with level on regular DataFrame
- df = tm.makeTimeDataFrame()
- with pytest.raises(TypeError, match='hierarchical'):
- df.count(level=0)
- self.frame['D'] = 'foo'
- result = self.frame.count(level=0, numeric_only=True)
- tm.assert_index_equal(result.columns, Index(list('ABC'), name='exp'))
- def test_count_level_series(self):
- index = MultiIndex(levels=[['foo', 'bar', 'baz'], ['one', 'two',
- 'three', 'four']],
- codes=[[0, 0, 0, 2, 2], [2, 0, 1, 1, 2]])
- s = Series(np.random.randn(len(index)), index=index)
- result = s.count(level=0)
- expected = s.groupby(level=0).count()
- tm.assert_series_equal(
- result.astype('f8'), expected.reindex(result.index).fillna(0))
- result = s.count(level=1)
- expected = s.groupby(level=1).count()
- tm.assert_series_equal(
- result.astype('f8'), expected.reindex(result.index).fillna(0))
- def test_count_level_corner(self):
- s = self.frame['A'][:0]
- result = s.count(level=0)
- expected = Series(0, index=s.index.levels[0], name='A')
- tm.assert_series_equal(result, expected)
- df = self.frame[:0]
- result = df.count(level=0)
- expected = DataFrame({}, index=s.index.levels[0],
- columns=df.columns).fillna(0).astype(np.int64)
- tm.assert_frame_equal(result, expected)
- def test_get_level_number_out_of_bounds(self):
- with pytest.raises(IndexError, match="Too many levels"):
- self.frame.index._get_level_number(2)
- with pytest.raises(IndexError, match="not a valid level number"):
- self.frame.index._get_level_number(-3)
- def test_unstack(self):
- # just check that it works for now
- unstacked = self.ymd.unstack()
- unstacked.unstack()
- # test that ints work
- self.ymd.astype(int).unstack()
- # test that int32 work
- self.ymd.astype(np.int32).unstack()
- def test_unstack_multiple_no_empty_columns(self):
- index = MultiIndex.from_tuples([(0, 'foo', 0), (0, 'bar', 0), (
- 1, 'baz', 1), (1, 'qux', 1)])
- s = Series(np.random.randn(4), index=index)
- unstacked = s.unstack([1, 2])
- expected = unstacked.dropna(axis=1, how='all')
- tm.assert_frame_equal(unstacked, expected)
- def test_stack(self):
- # regular roundtrip
- unstacked = self.ymd.unstack()
- restacked = unstacked.stack()
- tm.assert_frame_equal(restacked, self.ymd)
- unlexsorted = self.ymd.sort_index(level=2)
- unstacked = unlexsorted.unstack(2)
- restacked = unstacked.stack()
- tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)
- unlexsorted = unlexsorted[::-1]
- unstacked = unlexsorted.unstack(1)
- restacked = unstacked.stack().swaplevel(1, 2)
- tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)
- unlexsorted = unlexsorted.swaplevel(0, 1)
- unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
- restacked = unstacked.stack(0).swaplevel(1, 2)
- tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)
- # columns unsorted
- unstacked = self.ymd.unstack()
- unstacked = unstacked.sort_index(axis=1, ascending=False)
- restacked = unstacked.stack()
- tm.assert_frame_equal(restacked, self.ymd)
- # more than 2 levels in the columns
- unstacked = self.ymd.unstack(1).unstack(1)
- result = unstacked.stack(1)
- expected = self.ymd.unstack()
- tm.assert_frame_equal(result, expected)
- result = unstacked.stack(2)
- expected = self.ymd.unstack(1)
- tm.assert_frame_equal(result, expected)
- result = unstacked.stack(0)
- expected = self.ymd.stack().unstack(1).unstack(1)
- tm.assert_frame_equal(result, expected)
- # not all levels present in each echelon
- unstacked = self.ymd.unstack(2).loc[:, ::3]
- stacked = unstacked.stack().stack()
- ymd_stacked = self.ymd.stack()
- tm.assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))
- # stack with negative number
- result = self.ymd.unstack(0).stack(-2)
- expected = self.ymd.unstack(0).stack(0)
- # GH10417
- def check(left, right):
- tm.assert_series_equal(left, right)
- assert left.index.is_unique is False
- li, ri = left.index, right.index
- tm.assert_index_equal(li, ri)
- df = DataFrame(np.arange(12).reshape(4, 3),
- index=list('abab'),
- columns=['1st', '2nd', '3rd'])
- mi = MultiIndex(levels=[['a', 'b'], ['1st', '2nd', '3rd']],
- codes=[np.tile(
- np.arange(2).repeat(3), 2), np.tile(
- np.arange(3), 4)])
- left, right = df.stack(), Series(np.arange(12), index=mi)
- check(left, right)
- df.columns = ['1st', '2nd', '1st']
- mi = MultiIndex(levels=[['a', 'b'], ['1st', '2nd']], codes=[np.tile(
- np.arange(2).repeat(3), 2), np.tile(
- [0, 1, 0], 4)])
- left, right = df.stack(), Series(np.arange(12), index=mi)
- check(left, right)
- tpls = ('a', 2), ('b', 1), ('a', 1), ('b', 2)
- df.index = MultiIndex.from_tuples(tpls)
- mi = MultiIndex(levels=[['a', 'b'], [1, 2], ['1st', '2nd']],
- codes=[np.tile(
- np.arange(2).repeat(3), 2), np.repeat(
- [1, 0, 1], [3, 6, 3]), np.tile(
- [0, 1, 0], 4)])
- left, right = df.stack(), Series(np.arange(12), index=mi)
- check(left, right)
- def test_unstack_odd_failure(self):
- data = """day,time,smoker,sum,len
- Fri,Dinner,No,8.25,3.
- Fri,Dinner,Yes,27.03,9
- Fri,Lunch,No,3.0,1
- Fri,Lunch,Yes,13.68,6
- Sat,Dinner,No,139.63,45
- Sat,Dinner,Yes,120.77,42
- Sun,Dinner,No,180.57,57
- Sun,Dinner,Yes,66.82,19
- Thur,Dinner,No,3.0,1
- Thur,Lunch,No,117.32,44
- Thur,Lunch,Yes,51.51,17"""
- df = pd.read_csv(StringIO(data)).set_index(['day', 'time', 'smoker'])
- # it works, #2100
- result = df.unstack(2)
- recons = result.stack()
- tm.assert_frame_equal(recons, df)
- def test_stack_mixed_dtype(self):
- df = self.frame.T
- df['foo', 'four'] = 'foo'
- df = df.sort_index(level=1, axis=1)
- stacked = df.stack()
- result = df['foo'].stack().sort_index()
- tm.assert_series_equal(stacked['foo'], result, check_names=False)
- assert result.name is None
- assert stacked['bar'].dtype == np.float_
- def test_unstack_bug(self):
- df = DataFrame({'state': ['naive', 'naive', 'naive', 'activ', 'activ',
- 'activ'],
- 'exp': ['a', 'b', 'b', 'b', 'a', 'a'],
- 'barcode': [1, 2, 3, 4, 1, 3],
- 'v': ['hi', 'hi', 'bye', 'bye', 'bye', 'peace'],
- 'extra': np.arange(6.)})
- result = df.groupby(['state', 'exp', 'barcode', 'v']).apply(len)
- unstacked = result.unstack()
- restacked = unstacked.stack()
- tm.assert_series_equal(
- restacked, result.reindex(restacked.index).astype(float))
- def test_stack_unstack_preserve_names(self):
- unstacked = self.frame.unstack()
- assert unstacked.index.name == 'first'
- assert unstacked.columns.names == ['exp', 'second']
- restacked = unstacked.stack()
- assert restacked.index.names == self.frame.index.names
- def test_unstack_level_name(self):
- result = self.frame.unstack('second')
- expected = self.frame.unstack(level=1)
- tm.assert_frame_equal(result, expected)
- def test_stack_level_name(self):
- unstacked = self.frame.unstack('second')
- result = unstacked.stack('exp')
- expected = self.frame.unstack().stack(0)
- tm.assert_frame_equal(result, expected)
- result = self.frame.stack('exp')
- expected = self.frame.stack()
- tm.assert_series_equal(result, expected)
- def test_stack_unstack_multiple(self):
- unstacked = self.ymd.unstack(['year', 'month'])
- expected = self.ymd.unstack('year').unstack('month')
- tm.assert_frame_equal(unstacked, expected)
- assert unstacked.columns.names == expected.columns.names
- # series
- s = self.ymd['A']
- s_unstacked = s.unstack(['year', 'month'])
- tm.assert_frame_equal(s_unstacked, expected['A'])
- restacked = unstacked.stack(['year', 'month'])
- restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
- restacked = restacked.sort_index(level=0)
- tm.assert_frame_equal(restacked, self.ymd)
- assert restacked.index.names == self.ymd.index.names
- # GH #451
- unstacked = self.ymd.unstack([1, 2])
- expected = self.ymd.unstack(1).unstack(1).dropna(axis=1, how='all')
- tm.assert_frame_equal(unstacked, expected)
- unstacked = self.ymd.unstack([2, 1])
- expected = self.ymd.unstack(2).unstack(1).dropna(axis=1, how='all')
- tm.assert_frame_equal(unstacked, expected.loc[:, unstacked.columns])
- def test_stack_names_and_numbers(self):
- unstacked = self.ymd.unstack(['year', 'month'])
- # Can't use mixture of names and numbers to stack
- with pytest.raises(ValueError, match="level should contain"):
- unstacked.stack([0, 'month'])
- def test_stack_multiple_out_of_bounds(self):
- # nlevels == 3
- unstacked = self.ymd.unstack(['year', 'month'])
- with pytest.raises(IndexError, match="Too many levels"):
- unstacked.stack([2, 3])
- with pytest.raises(IndexError, match="not a valid level number"):
- unstacked.stack([-4, -3])
- def test_unstack_period_series(self):
- # GH 4342
- idx1 = pd.PeriodIndex(['2013-01', '2013-01', '2013-02', '2013-02',
- '2013-03', '2013-03'], freq='M', name='period')
- idx2 = Index(['A', 'B'] * 3, name='str')
- value = [1, 2, 3, 4, 5, 6]
- idx = MultiIndex.from_arrays([idx1, idx2])
- s = Series(value, index=idx)
- result1 = s.unstack()
- result2 = s.unstack(level=1)
- result3 = s.unstack(level=0)
- e_idx = pd.PeriodIndex(
- ['2013-01', '2013-02', '2013-03'], freq='M', name='period')
- expected = DataFrame({'A': [1, 3, 5], 'B': [2, 4, 6]}, index=e_idx,
- columns=['A', 'B'])
- expected.columns.name = 'str'
- tm.assert_frame_equal(result1, expected)
- tm.assert_frame_equal(result2, expected)
- tm.assert_frame_equal(result3, expected.T)
- idx1 = pd.PeriodIndex(['2013-01', '2013-01', '2013-02', '2013-02',
- '2013-03', '2013-03'], freq='M', name='period1')
- idx2 = pd.PeriodIndex(['2013-12', '2013-11', '2013-10', '2013-09',
- '2013-08', '2013-07'], freq='M', name='period2')
- idx = MultiIndex.from_arrays([idx1, idx2])
- s = Series(value, index=idx)
- result1 = s.unstack()
- result2 = s.unstack(level=1)
- result3 = s.unstack(level=0)
- e_idx = pd.PeriodIndex(
- ['2013-01', '2013-02', '2013-03'], freq='M', name='period1')
- e_cols = pd.PeriodIndex(['2013-07', '2013-08', '2013-09', '2013-10',
- '2013-11', '2013-12'],
- freq='M', name='period2')
- expected = DataFrame([[np.nan, np.nan, np.nan, np.nan, 2, 1],
- [np.nan, np.nan, 4, 3, np.nan, np.nan],
- [6, 5, np.nan, np.nan, np.nan, np.nan]],
- index=e_idx, columns=e_cols)
- tm.assert_frame_equal(result1, expected)
- tm.assert_frame_equal(result2, expected)
- tm.assert_frame_equal(result3, expected.T)
- def test_unstack_period_frame(self):
- # GH 4342
- idx1 = pd.PeriodIndex(['2014-01', '2014-02', '2014-02', '2014-02',
- '2014-01', '2014-01'],
- freq='M', name='period1')
- idx2 = pd.PeriodIndex(['2013-12', '2013-12', '2014-02', '2013-10',
- '2013-10', '2014-02'],
- freq='M', name='period2')
- value = {'A': [1, 2, 3, 4, 5, 6], 'B': [6, 5, 4, 3, 2, 1]}
- idx = MultiIndex.from_arrays([idx1, idx2])
- df = DataFrame(value, index=idx)
- result1 = df.unstack()
- result2 = df.unstack(level=1)
- result3 = df.unstack(level=0)
- e_1 = pd.PeriodIndex(['2014-01', '2014-02'], freq='M', name='period1')
- e_2 = pd.PeriodIndex(['2013-10', '2013-12', '2014-02', '2013-10',
- '2013-12', '2014-02'], freq='M', name='period2')
- e_cols = MultiIndex.from_arrays(['A A A B B B'.split(), e_2])
- expected = DataFrame([[5, 1, 6, 2, 6, 1], [4, 2, 3, 3, 5, 4]],
- index=e_1, columns=e_cols)
- tm.assert_frame_equal(result1, expected)
- tm.assert_frame_equal(result2, expected)
- e_1 = pd.PeriodIndex(['2014-01', '2014-02', '2014-01',
- '2014-02'], freq='M', name='period1')
- e_2 = pd.PeriodIndex(
- ['2013-10', '2013-12', '2014-02'], freq='M', name='period2')
- e_cols = MultiIndex.from_arrays(['A A B B'.split(), e_1])
- expected = DataFrame([[5, 4, 2, 3], [1, 2, 6, 5], [6, 3, 1, 4]],
- index=e_2, columns=e_cols)
- tm.assert_frame_equal(result3, expected)
- def test_stack_multiple_bug(self):
- """ bug when some uniques are not present in the data #3170"""
- id_col = ([1] * 3) + ([2] * 3)
- name = (['a'] * 3) + (['b'] * 3)
- date = pd.to_datetime(['2013-01-03', '2013-01-04', '2013-01-05'] * 2)
- var1 = np.random.randint(0, 100, 6)
- df = DataFrame(dict(ID=id_col, NAME=name, DATE=date, VAR1=var1))
- multi = df.set_index(['DATE', 'ID'])
- multi.columns.name = 'Params'
- unst = multi.unstack('ID')
- down = unst.resample('W-THU').mean()
- rs = down.stack('ID')
- xp = unst.loc[:, ['VAR1']].resample('W-THU').mean().stack('ID')
- xp.columns.name = 'Params'
- tm.assert_frame_equal(rs, xp)
- def test_stack_dropna(self):
- # GH #3997
- df = DataFrame({'A': ['a1', 'a2'], 'B': ['b1', 'b2'], 'C': [1, 1]})
- df = df.set_index(['A', 'B'])
- stacked = df.unstack().stack(dropna=False)
- assert len(stacked) > len(stacked.dropna())
- stacked = df.unstack().stack(dropna=True)
- tm.assert_frame_equal(stacked, stacked.dropna())
- def test_unstack_multiple_hierarchical(self):
- df = DataFrame(index=[[0, 0, 0, 0, 1, 1, 1, 1],
- [0, 0, 1, 1, 0, 0, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1
- ]],
- columns=[[0, 0, 1, 1], [0, 1, 0, 1]])
- df.index.names = ['a', 'b', 'c']
- df.columns.names = ['d', 'e']
- # it works!
- df.unstack(['b', 'c'])
- def test_groupby_transform(self):
- s = self.frame['A']
- grouper = s.index.get_level_values(0)
- grouped = s.groupby(grouper)
- applied = grouped.apply(lambda x: x * 2)
- expected = grouped.transform(lambda x: x * 2)
- result = applied.reindex(expected.index)
- tm.assert_series_equal(result, expected, check_names=False)
- def test_unstack_sparse_keyspace(self):
- # memory problems with naive impl #2278
- # Generate Long File & Test Pivot
- NUM_ROWS = 1000
- df = DataFrame({'A': np.random.randint(100, size=NUM_ROWS),
- 'B': np.random.randint(300, size=NUM_ROWS),
- 'C': np.random.randint(-7, 7, size=NUM_ROWS),
- 'D': np.random.randint(-19, 19, size=NUM_ROWS),
- 'E': np.random.randint(3000, size=NUM_ROWS),
- 'F': np.random.randn(NUM_ROWS)})
- idf = df.set_index(['A', 'B', 'C', 'D', 'E'])
- # it works! is sufficient
- idf.unstack('E')
- def test_unstack_unobserved_keys(self):
- # related to #2278 refactoring
- levels = [[0, 1], [0, 1, 2, 3]]
- codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
- index = MultiIndex(levels, codes)
- df = DataFrame(np.random.randn(4, 2), index=index)
- result = df.unstack()
- assert len(result.columns) == 4
- recons = result.stack()
- tm.assert_frame_equal(recons, df)
- @pytest.mark.slow
- def test_unstack_number_of_levels_larger_than_int32(self):
- # GH 20601
- df = DataFrame(np.random.randn(2 ** 16, 2),
- index=[np.arange(2 ** 16), np.arange(2 ** 16)])
- with pytest.raises(ValueError, match='int32 overflow'):
- df.unstack()
- def test_stack_order_with_unsorted_levels(self):
- # GH 16323
- def manual_compare_stacked(df, df_stacked, lev0, lev1):
- assert all(df.loc[row, col] ==
- df_stacked.loc[(row, col[lev0]), col[lev1]]
- for row in df.index for col in df.columns)
- # deep check for 1-row case
- for width in [2, 3]:
- levels_poss = itertools.product(
- itertools.permutations([0, 1, 2], width),
- repeat=2)
- for levels in levels_poss:
- columns = MultiIndex(levels=levels,
- codes=[[0, 0, 1, 1],
- [0, 1, 0, 1]])
- df = DataFrame(columns=columns, data=[range(4)])
- for stack_lev in range(2):
- df_stacked = df.stack(stack_lev)
- manual_compare_stacked(df, df_stacked,
- stack_lev, 1 - stack_lev)
- # check multi-row case
- mi = MultiIndex(levels=[["A", "C", "B"], ["B", "A", "C"]],
- codes=[np.repeat(range(3), 3), np.tile(range(3), 3)])
- df = DataFrame(columns=mi, index=range(5),
- data=np.arange(5 * len(mi)).reshape(5, -1))
- manual_compare_stacked(df, df.stack(0), 0, 1)
- def test_groupby_corner(self):
- midx = MultiIndex(levels=[['foo'], ['bar'], ['baz']],
- codes=[[0], [0], [0]],
- names=['one', 'two', 'three'])
- df = DataFrame([np.random.rand(4)], columns=['a', 'b', 'c', 'd'],
- index=midx)
- # should work
- df.groupby(level='three')
- def test_groupby_level_no_obs(self):
- # #1697
- midx = MultiIndex.from_tuples([('f1', 's1'), ('f1', 's2'), (
- 'f2', 's1'), ('f2', 's2'), ('f3', 's1'), ('f3', 's2')])
- df = DataFrame(
- [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], columns=midx)
- df1 = df.loc(axis=1)[df.columns.map(
- lambda u: u[0] in ['f2', 'f3'])]
- grouped = df1.groupby(axis=1, level=0)
- result = grouped.sum()
- assert (result.columns == ['f2', 'f3']).all()
- def test_join(self):
- a = self.frame.loc[self.frame.index[:5], ['A']]
- b = self.frame.loc[self.frame.index[2:], ['B', 'C']]
- joined = a.join(b, how='outer').reindex(self.frame.index)
- expected = self.frame.copy()
- expected.values[np.isnan(joined.values)] = np.nan
- assert not np.isnan(joined.values).all()
- # TODO what should join do with names ?
- tm.assert_frame_equal(joined, expected, check_names=False)
- def test_swaplevel(self):
- swapped = self.frame['A'].swaplevel()
- swapped2 = self.frame['A'].swaplevel(0)
- swapped3 = self.frame['A'].swaplevel(0, 1)
- swapped4 = self.frame['A'].swaplevel('first', 'second')
- assert not swapped.index.equals(self.frame.index)
- tm.assert_series_equal(swapped, swapped2)
- tm.assert_series_equal(swapped, swapped3)
- tm.assert_series_equal(swapped, swapped4)
- back = swapped.swaplevel()
- back2 = swapped.swaplevel(0)
- back3 = swapped.swaplevel(0, 1)
- back4 = swapped.swaplevel('second', 'first')
- assert back.index.equals(self.frame.index)
- tm.assert_series_equal(back, back2)
- tm.assert_series_equal(back, back3)
- tm.assert_series_equal(back, back4)
- ft = self.frame.T
- swapped = ft.swaplevel('first', 'second', axis=1)
- exp = self.frame.swaplevel('first', 'second').T
- tm.assert_frame_equal(swapped, exp)
- def test_reorder_levels(self):
- result = self.ymd.reorder_levels(['month', 'day', 'year'])
- expected = self.ymd.swaplevel(0, 1).swaplevel(1, 2)
- tm.assert_frame_equal(result, expected)
- result = self.ymd['A'].reorder_levels(['month', 'day', 'year'])
- expected = self.ymd['A'].swaplevel(0, 1).swaplevel(1, 2)
- tm.assert_series_equal(result, expected)
- result = self.ymd.T.reorder_levels(['month', 'day', 'year'], axis=1)
- expected = self.ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
- tm.assert_frame_equal(result, expected)
- with pytest.raises(TypeError, match='hierarchical axis'):
- self.ymd.reorder_levels([1, 2], axis=1)
- with pytest.raises(IndexError, match='Too many levels'):
- self.ymd.index.reorder_levels([1, 2, 3])
- def test_insert_index(self):
- df = self.ymd[:5].T
- df[2000, 1, 10] = df[2000, 1, 7]
- assert isinstance(df.columns, MultiIndex)
- assert (df[2000, 1, 10] == df[2000, 1, 7]).all()
- def test_alignment(self):
- x = Series(data=[1, 2, 3], index=MultiIndex.from_tuples([("A", 1), (
- "A", 2), ("B", 3)]))
- y = Series(data=[4, 5, 6], index=MultiIndex.from_tuples([("Z", 1), (
- "Z", 2), ("B", 3)]))
- res = x - y
- exp_index = x.index.union(y.index)
- exp = x.reindex(exp_index) - y.reindex(exp_index)
- tm.assert_series_equal(res, exp)
- # hit non-monotonic code path
- res = x[::-1] - y[::-1]
- exp_index = x.index.union(y.index)
- exp = x.reindex(exp_index) - y.reindex(exp_index)
- tm.assert_series_equal(res, exp)
- def test_count(self):
- frame = self.frame.copy()
- frame.index.names = ['a', 'b']
- result = frame.count(level='b')
- expect = self.frame.count(level=1)
- tm.assert_frame_equal(result, expect, check_names=False)
- result = frame.count(level='a')
- expect = self.frame.count(level=0)
- tm.assert_frame_equal(result, expect, check_names=False)
- series = self.series.copy()
- series.index.names = ['a', 'b']
- result = series.count(level='b')
- expect = self.series.count(level=1)
- tm.assert_series_equal(result, expect, check_names=False)
- assert result.index.name == 'b'
- result = series.count(level='a')
- expect = self.series.count(level=0)
- tm.assert_series_equal(result, expect, check_names=False)
- assert result.index.name == 'a'
- msg = "Level x not found"
- with pytest.raises(KeyError, match=msg):
- series.count('x')
- with pytest.raises(KeyError, match=msg):
- frame.count(level='x')
- @pytest.mark.parametrize('op', AGG_FUNCTIONS)
- @pytest.mark.parametrize('level', [0, 1])
- @pytest.mark.parametrize('skipna', [True, False])
- @pytest.mark.parametrize('sort', [True, False])
- def test_series_group_min_max(self, op, level, skipna, sort):
- # GH 17537
- grouped = self.series.groupby(level=level, sort=sort)
- # skipna=True
- leftside = grouped.agg(lambda x: getattr(x, op)(skipna=skipna))
- rightside = getattr(self.series, op)(level=level, skipna=skipna)
- if sort:
- rightside = rightside.sort_index(level=level)
- tm.assert_series_equal(leftside, rightside)
- @pytest.mark.parametrize('op', AGG_FUNCTIONS)
- @pytest.mark.parametrize('level', [0, 1])
- @pytest.mark.parametrize('axis', [0, 1])
- @pytest.mark.parametrize('skipna', [True, False])
- @pytest.mark.parametrize('sort', [True, False])
- def test_frame_group_ops(self, op, level, axis, skipna, sort):
- # GH 17537
- self.frame.iloc[1, [1, 2]] = np.nan
- self.frame.iloc[7, [0, 1]] = np.nan
- if axis == 0:
- frame = self.frame
- else:
- frame = self.frame.T
- grouped = frame.groupby(level=level, axis=axis, sort=sort)
- pieces = []
- def aggf(x):
- pieces.append(x)
- return getattr(x, op)(skipna=skipna, axis=axis)
- leftside = grouped.agg(aggf)
- rightside = getattr(frame, op)(level=level, axis=axis,
- skipna=skipna)
- if sort:
- rightside = rightside.sort_index(level=level, axis=axis)
- frame = frame.sort_index(level=level, axis=axis)
- # for good measure, groupby detail
- level_index = frame._get_axis(axis).levels[level]
- tm.assert_index_equal(leftside._get_axis(axis), level_index)
- tm.assert_index_equal(rightside._get_axis(axis), level_index)
- tm.assert_frame_equal(leftside, rightside)
- def test_stat_op_corner(self):
- obj = Series([10.0], index=MultiIndex.from_tuples([(2, 3)]))
- result = obj.sum(level=0)
- expected = Series([10.0], index=[2])
- tm.assert_series_equal(result, expected)
- def test_frame_any_all_group(self):
- df = DataFrame(
- {'data': [False, False, True, False, True, False, True]},
- index=[
- ['one', 'one', 'two', 'one', 'two', 'two', 'two'],
- [0, 1, 0, 2, 1, 2, 3]])
- result = df.any(level=0)
- ex = DataFrame({'data': [False, True]}, index=['one', 'two'])
- tm.assert_frame_equal(result, ex)
- result = df.all(level=0)
- ex = DataFrame({'data': [False, False]}, index=['one', 'two'])
- tm.assert_frame_equal(result, ex)
- def test_std_var_pass_ddof(self):
- index = MultiIndex.from_arrays([np.arange(5).repeat(10), np.tile(
- np.arange(10), 5)])
- df = DataFrame(np.random.randn(len(index), 5), index=index)
- for meth in ['var', 'std']:
- ddof = 4
- alt = lambda x: getattr(x, meth)(ddof=ddof)
- result = getattr(df[0], meth)(level=0, ddof=ddof)
- expected = df[0].groupby(level=0).agg(alt)
- tm.assert_series_equal(result, expected)
- result = getattr(df, meth)(level=0, ddof=ddof)
- expected = df.groupby(level=0).agg(alt)
- tm.assert_frame_equal(result, expected)
- def test_frame_series_agg_multiple_levels(self):
- result = self.ymd.sum(level=['year', 'month'])
- expected = self.ymd.groupby(level=['year', 'month']).sum()
- tm.assert_frame_equal(result, expected)
- result = self.ymd['A'].sum(level=['year', 'month'])
- expected = self.ymd['A'].groupby(level=['year', 'month']).sum()
- tm.assert_series_equal(result, expected)
- def test_groupby_multilevel(self):
- result = self.ymd.groupby(level=[0, 1]).mean()
- k1 = self.ymd.index.get_level_values(0)
- k2 = self.ymd.index.get_level_values(1)
- expected = self.ymd.groupby([k1, k2]).mean()
- # TODO groupby with level_values drops names
- tm.assert_frame_equal(result, expected, check_names=False)
- assert result.index.names == self.ymd.index.names[:2]
- result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
- tm.assert_frame_equal(result, result2)
- def test_groupby_multilevel_with_transform(self):
- pass
- def test_multilevel_consolidate(self):
- index = MultiIndex.from_tuples([('foo', 'one'), ('foo', 'two'), (
- 'bar', 'one'), ('bar', 'two')])
- df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
- df['Totals', ''] = df.sum(1)
- df = df._consolidate()
- def test_ix_preserve_names(self):
- result = self.ymd.loc[2000]
- result2 = self.ymd['A'].loc[2000]
- assert result.index.names == self.ymd.index.names[1:]
- assert result2.index.names == self.ymd.index.names[1:]
- result = self.ymd.loc[2000, 2]
- result2 = self.ymd['A'].loc[2000, 2]
- assert result.index.name == self.ymd.index.names[2]
- assert result2.index.name == self.ymd.index.names[2]
- def test_unstack_preserve_types(self):
- # GH #403
- self.ymd['E'] = 'foo'
- self.ymd['F'] = 2
- unstacked = self.ymd.unstack('month')
- assert unstacked['A', 1].dtype == np.float64
- assert unstacked['E', 1].dtype == np.object_
- assert unstacked['F', 1].dtype == np.float64
- def test_unstack_group_index_overflow(self):
- codes = np.tile(np.arange(500), 2)
- level = np.arange(500)
- index = MultiIndex(levels=[level] * 8 + [[0, 1]],
- codes=[codes] * 8 + [np.arange(2).repeat(500)])
- s = Series(np.arange(1000), index=index)
- result = s.unstack()
- assert result.shape == (500, 2)
- # test roundtrip
- stacked = result.stack()
- tm.assert_series_equal(s, stacked.reindex(s.index))
- # put it at beginning
- index = MultiIndex(levels=[[0, 1]] + [level] * 8,
- codes=[np.arange(2).repeat(500)] + [codes] * 8)
- s = Series(np.arange(1000), index=index)
- result = s.unstack(0)
- assert result.shape == (500, 2)
- # put it in middle
- index = MultiIndex(levels=[level] * 4 + [[0, 1]] + [level] * 4,
- codes=([codes] * 4 + [np.arange(2).repeat(500)] +
- [codes] * 4))
- s = Series(np.arange(1000), index=index)
- result = s.unstack(4)
- assert result.shape == (500, 2)
- def test_pyint_engine(self):
- # GH 18519 : when combinations of codes cannot be represented in 64
- # bits, the index underlying the MultiIndex engine works with Python
- # integers, rather than uint64.
- N = 5
- keys = [tuple(l) for l in [[0] * 10 * N,
- [1] * 10 * N,
- [2] * 10 * N,
- [np.nan] * N + [2] * 9 * N,
- [0] * N + [2] * 9 * N,
- [np.nan] * N + [2] * 8 * N + [0] * N]]
- # Each level contains 4 elements (including NaN), so it is represented
- # in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a
- # 64 bit engine and truncating the first levels, the fourth and fifth
- # keys would collide; if truncating the last levels, the fifth and
- # sixth; if rotating bits rather than shifting, the third and fifth.
- for idx in range(len(keys)):
- index = MultiIndex.from_tuples(keys)
- assert index.get_loc(keys[idx]) == idx
- expected = np.arange(idx + 1, dtype=np.intp)
- result = index.get_indexer([keys[i] for i in expected])
- tm.assert_numpy_array_equal(result, expected)
- # With missing key:
- idces = range(len(keys))
- expected = np.array([-1] + list(idces), dtype=np.intp)
- missing = tuple([0, 1] * 5 * N)
- result = index.get_indexer([missing] + [keys[i] for i in idces])
- tm.assert_numpy_array_equal(result, expected)
- def test_to_html(self):
- self.ymd.columns.name = 'foo'
- self.ymd.to_html()
- self.ymd.T.to_html()
- def test_level_with_tuples(self):
- index = MultiIndex(levels=[[('foo', 'bar', 0), ('foo', 'baz', 0), (
- 'foo', 'qux', 0)], [0, 1]],
- codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])
- series = Series(np.random.randn(6), index=index)
- frame = DataFrame(np.random.randn(6, 4), index=index)
- result = series[('foo', 'bar', 0)]
- result2 = series.loc[('foo', 'bar', 0)]
- expected = series[:2]
- expected.index = expected.index.droplevel(0)
- tm.assert_series_equal(result, expected)
- tm.assert_series_equal(result2, expected)
- with pytest.raises(KeyError, match=r"^\(\('foo', 'bar', 0\), 2\)$"):
- series[('foo', 'bar', 0), 2]
- result = frame.loc[('foo', 'bar', 0)]
- result2 = frame.xs(('foo', 'bar', 0))
- expected = frame[:2]
- expected.index = expected.index.droplevel(0)
- tm.assert_frame_equal(result, expected)
- tm.assert_frame_equal(result2, expected)
- index = MultiIndex(levels=[[('foo', 'bar'), ('foo', 'baz'), (
- 'foo', 'qux')], [0, 1]],
- codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])
- series = Series(np.random.randn(6), index=index)
- frame = DataFrame(np.random.randn(6, 4), index=index)
- result = series[('foo', 'bar')]
- result2 = series.loc[('foo', 'bar')]
- expected = series[:2]
- expected.index = expected.index.droplevel(0)
- tm.assert_series_equal(result, expected)
- tm.assert_series_equal(result2, expected)
- result = frame.loc[('foo', 'bar')]
- result2 = frame.xs(('foo', 'bar'))
- expected = frame[:2]
- expected.index = expected.index.droplevel(0)
- tm.assert_frame_equal(result, expected)
- tm.assert_frame_equal(result2, expected)
- def test_mixed_depth_drop(self):
- arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
- ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
- ['', 'wx', 'wy', '', '', '']]
- tuples = sorted(zip(*arrays))
- index = MultiIndex.from_tuples(tuples)
- df = DataFrame(randn(4, 6), columns=index)
- result = df.drop('a', axis=1)
- expected = df.drop([('a', '', '')], axis=1)
- tm.assert_frame_equal(expected, result)
- result = df.drop(['top'], axis=1)
- expected = df.drop([('top', 'OD', 'wx')], axis=1)
- expected = expected.drop([('top', 'OD', 'wy')], axis=1)
- tm.assert_frame_equal(expected, result)
- result = df.drop(('top', 'OD', 'wx'), axis=1)
- expected = df.drop([('top', 'OD', 'wx')], axis=1)
- tm.assert_frame_equal(expected, result)
- expected = df.drop([('top', 'OD', 'wy')], axis=1)
- expected = df.drop('top', axis=1)
- result = df.drop('result1', level=1, axis=1)
- expected = df.drop([('routine1', 'result1', ''),
- ('routine2', 'result1', '')], axis=1)
- tm.assert_frame_equal(expected, result)
- def test_drop_nonunique(self):
- df = DataFrame([["x-a", "x", "a", 1.5], ["x-a", "x", "a", 1.2],
- ["z-c", "z", "c", 3.1], ["x-a", "x", "a", 4.1],
- ["x-b", "x", "b", 5.1], ["x-b", "x", "b", 4.1],
- ["x-b", "x", "b", 2.2],
- ["y-a", "y", "a", 1.2], ["z-b", "z", "b", 2.1]],
- columns=["var1", "var2", "var3", "var4"])
- grp_size = df.groupby("var1").size()
- drop_idx = grp_size.loc[grp_size == 1]
- idf = df.set_index(["var1", "var2", "var3"])
- # it works! #2101
- result = idf.drop(drop_idx.index, level=0).reset_index()
- expected = df[-df.var1.isin(drop_idx.index)]
- result.index = expected.index
- tm.assert_frame_equal(result, expected)
- def test_mixed_depth_pop(self):
- arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
- ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
- ['', 'wx', 'wy', '', '', '']]
- tuples = sorted(zip(*arrays))
- index = MultiIndex.from_tuples(tuples)
- df = DataFrame(randn(4, 6), columns=index)
- df1 = df.copy()
- df2 = df.copy()
- result = df1.pop('a')
- expected = df2.pop(('a', '', ''))
- tm.assert_series_equal(expected, result, check_names=False)
- tm.assert_frame_equal(df1, df2)
- assert result.name == 'a'
- expected = df1['top']
- df1 = df1.drop(['top'], axis=1)
- result = df2.pop('top')
- tm.assert_frame_equal(expected, result)
- tm.assert_frame_equal(df1, df2)
- def test_reindex_level_partial_selection(self):
- result = self.frame.reindex(['foo', 'qux'], level=0)
- expected = self.frame.iloc[[0, 1, 2, 7, 8, 9]]
- tm.assert_frame_equal(result, expected)
- result = self.frame.T.reindex(['foo', 'qux'], axis=1, level=0)
- tm.assert_frame_equal(result, expected.T)
- result = self.frame.loc[['foo', 'qux']]
- tm.assert_frame_equal(result, expected)
- result = self.frame['A'].loc[['foo', 'qux']]
- tm.assert_series_equal(result, expected['A'])
- result = self.frame.T.loc[:, ['foo', 'qux']]
- tm.assert_frame_equal(result, expected.T)
- def test_drop_level(self):
- result = self.frame.drop(['bar', 'qux'], level='first')
- expected = self.frame.iloc[[0, 1, 2, 5, 6]]
- tm.assert_frame_equal(result, expected)
- result = self.frame.drop(['two'], level='second')
- expected = self.frame.iloc[[0, 2, 3, 6, 7, 9]]
- tm.assert_frame_equal(result, expected)
- result = self.frame.T.drop(['bar', 'qux'], axis=1, level='first')
- expected = self.frame.iloc[[0, 1, 2, 5, 6]].T
- tm.assert_frame_equal(result, expected)
- result = self.frame.T.drop(['two'], axis=1, level='second')
- expected = self.frame.iloc[[0, 2, 3, 6, 7, 9]].T
- tm.assert_frame_equal(result, expected)
- def test_drop_level_nonunique_datetime(self):
- # GH 12701
- idx = Index([2, 3, 4, 4, 5], name='id')
- idxdt = pd.to_datetime(['201603231400',
- '201603231500',
- '201603231600',
- '201603231600',
- '201603231700'])
- df = DataFrame(np.arange(10).reshape(5, 2),
- columns=list('ab'), index=idx)
- df['tstamp'] = idxdt
- df = df.set_index('tstamp', append=True)
- ts = Timestamp('201603231600')
- assert df.index.is_unique is False
- result = df.drop(ts, level='tstamp')
- expected = df.loc[idx != 4]
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize('box', [Series, DataFrame])
- def test_drop_tz_aware_timestamp_across_dst(self, box):
- # GH 21761
- start = Timestamp('2017-10-29', tz='Europe/Berlin')
- end = Timestamp('2017-10-29 04:00:00', tz='Europe/Berlin')
- index = pd.date_range(start, end, freq='15min')
- data = box(data=[1] * len(index), index=index)
- result = data.drop(start)
- expected_start = Timestamp('2017-10-29 00:15:00', tz='Europe/Berlin')
- expected_idx = pd.date_range(expected_start, end, freq='15min')
- expected = box(data=[1] * len(expected_idx), index=expected_idx)
- tm.assert_equal(result, expected)
- def test_drop_preserve_names(self):
- index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
- [1, 2, 3, 1, 2, 3]],
- names=['one', 'two'])
- df = DataFrame(np.random.randn(6, 3), index=index)
- result = df.drop([(0, 2)])
- assert result.index.names == ('one', 'two')
- def test_unicode_repr_issues(self):
- levels = [Index([u('a/\u03c3'), u('b/\u03c3'), u('c/\u03c3')]),
- Index([0, 1])]
- codes = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)]
- index = MultiIndex(levels=levels, codes=codes)
- repr(index.levels)
- # NumPy bug
- # repr(index.get_level_values(1))
- def test_unicode_repr_level_names(self):
- index = MultiIndex.from_tuples([(0, 0), (1, 1)],
- names=[u('\u0394'), 'i1'])
- s = Series(lrange(2), index=index)
- df = DataFrame(np.random.randn(2, 4), index=index)
- repr(s)
- repr(df)
- def test_join_segfault(self):
- # 1532
- df1 = DataFrame({'a': [1, 1], 'b': [1, 2], 'x': [1, 2]})
- df2 = DataFrame({'a': [2, 2], 'b': [1, 2], 'y': [1, 2]})
- df1 = df1.set_index(['a', 'b'])
- df2 = df2.set_index(['a', 'b'])
- # it works!
- for how in ['left', 'right', 'outer']:
- df1.join(df2, how=how)
- def test_frame_dict_constructor_empty_series(self):
- s1 = Series([
- 1, 2, 3, 4
- ], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)]))
- s2 = Series([
- 1, 2, 3, 4
- ], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)]))
- s3 = Series()
- # it works!
- DataFrame({'foo': s1, 'bar': s2, 'baz': s3})
- DataFrame.from_dict({'foo': s1, 'baz': s3, 'bar': s2})
- def test_multiindex_na_repr(self):
- # only an issue with long columns
- from numpy import nan
- df3 = DataFrame({
- 'A' * 30: {('A', 'A0006000', 'nuit'): 'A0006000'},
- 'B' * 30: {('A', 'A0006000', 'nuit'): nan},
- 'C' * 30: {('A', 'A0006000', 'nuit'): nan},
- 'D' * 30: {('A', 'A0006000', 'nuit'): nan},
- 'E' * 30: {('A', 'A0006000', 'nuit'): 'A'},
- 'F' * 30: {('A', 'A0006000', 'nuit'): nan},
- })
- idf = df3.set_index(['A' * 30, 'C' * 30])
- repr(idf)
- def test_assign_index_sequences(self):
- # #2200
- df = DataFrame({"a": [1, 2, 3],
- "b": [4, 5, 6],
- "c": [7, 8, 9]}).set_index(["a", "b"])
- index = list(df.index)
- index[0] = ("faz", "boo")
- df.index = index
- repr(df)
- # this travels an improper code path
- index[0] = ["faz", "boo"]
- df.index = index
- repr(df)
- def test_tuples_have_na(self):
- index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
- codes=[[1, 1, 1, 1, -1, 0, 0, 0],
- [0, 1, 2, 3, 0, 1, 2, 3]])
- assert isna(index[4][0])
- assert isna(index.values[4][0])
- def test_duplicate_groupby_issues(self):
- idx_tp = [('600809', '20061231'), ('600809', '20070331'),
- ('600809', '20070630'), ('600809', '20070331')]
- dt = ['demo', 'demo', 'demo', 'demo']
- idx = MultiIndex.from_tuples(idx_tp, names=['STK_ID', 'RPT_Date'])
- s = Series(dt, index=idx)
- result = s.groupby(s.index).first()
- assert len(result) == 3
- def test_duplicate_mi(self):
- # GH 4516
- df = DataFrame([['foo', 'bar', 1.0, 1], ['foo', 'bar', 2.0, 2],
- ['bah', 'bam', 3.0, 3],
- ['bah', 'bam', 4.0, 4], ['foo', 'bar', 5.0, 5],
- ['bah', 'bam', 6.0, 6]],
- columns=list('ABCD'))
- df = df.set_index(['A', 'B'])
- df = df.sort_index(level=0)
- expected = DataFrame([['foo', 'bar', 1.0, 1], ['foo', 'bar', 2.0, 2],
- ['foo', 'bar', 5.0, 5]],
- columns=list('ABCD')).set_index(['A', 'B'])
- result = df.loc[('foo', 'bar')]
- tm.assert_frame_equal(result, expected)
- def test_duplicated_drop_duplicates(self):
- # GH 4060
- idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))
- expected = np.array(
- [False, False, False, True, False, False], dtype=bool)
- duplicated = idx.duplicated()
- tm.assert_numpy_array_equal(duplicated, expected)
- assert duplicated.dtype == bool
- expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
- tm.assert_index_equal(idx.drop_duplicates(), expected)
- expected = np.array([True, False, False, False, False, False])
- duplicated = idx.duplicated(keep='last')
- tm.assert_numpy_array_equal(duplicated, expected)
- assert duplicated.dtype == bool
- expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
- tm.assert_index_equal(idx.drop_duplicates(keep='last'), expected)
- expected = np.array([True, False, False, True, False, False])
- duplicated = idx.duplicated(keep=False)
- tm.assert_numpy_array_equal(duplicated, expected)
- assert duplicated.dtype == bool
- expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
- tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
- def test_multiindex_set_index(self):
- # segfault in #3308
- d = {'t1': [2, 2.5, 3], 't2': [4, 5, 6]}
- df = DataFrame(d)
- tuples = [(0, 1), (0, 2), (1, 2)]
- df['tuples'] = tuples
- index = MultiIndex.from_tuples(df['tuples'])
- # it works!
- df.set_index(index)
- def test_datetimeindex(self):
- idx1 = pd.DatetimeIndex(
- ['2013-04-01 9:00', '2013-04-02 9:00', '2013-04-03 9:00'
- ] * 2, tz='Asia/Tokyo')
- idx2 = pd.date_range('2010/01/01', periods=6, freq='M',
- tz='US/Eastern')
- idx = MultiIndex.from_arrays([idx1, idx2])
- expected1 = pd.DatetimeIndex(['2013-04-01 9:00', '2013-04-02 9:00',
- '2013-04-03 9:00'], tz='Asia/Tokyo')
- tm.assert_index_equal(idx.levels[0], expected1)
- tm.assert_index_equal(idx.levels[1], idx2)
- # from datetime combos
- # GH 7888
- date1 = datetime.date.today()
- date2 = datetime.datetime.today()
- date3 = Timestamp.today()
- for d1, d2 in itertools.product(
- [date1, date2, date3], [date1, date2, date3]):
- index = MultiIndex.from_product([[d1], [d2]])
- assert isinstance(index.levels[0], pd.DatetimeIndex)
- assert isinstance(index.levels[1], pd.DatetimeIndex)
- def test_constructor_with_tz(self):
- index = pd.DatetimeIndex(['2013/01/01 09:00', '2013/01/02 09:00'],
- name='dt1', tz='US/Pacific')
- columns = pd.DatetimeIndex(['2014/01/01 09:00', '2014/01/02 09:00'],
- name='dt2', tz='Asia/Tokyo')
- result = MultiIndex.from_arrays([index, columns])
- tm.assert_index_equal(result.levels[0], index)
- tm.assert_index_equal(result.levels[1], columns)
- result = MultiIndex.from_arrays([Series(index), Series(columns)])
- tm.assert_index_equal(result.levels[0], index)
- tm.assert_index_equal(result.levels[1], columns)
- def test_set_index_datetime(self):
- # GH 3950
- df = DataFrame(
- {'label': ['a', 'a', 'a', 'b', 'b', 'b'],
- 'datetime': ['2011-07-19 07:00:00', '2011-07-19 08:00:00',
- '2011-07-19 09:00:00', '2011-07-19 07:00:00',
- '2011-07-19 08:00:00', '2011-07-19 09:00:00'],
- 'value': range(6)})
- df.index = pd.to_datetime(df.pop('datetime'), utc=True)
- df.index = df.index.tz_convert('US/Pacific')
- expected = pd.DatetimeIndex(['2011-07-19 07:00:00',
- '2011-07-19 08:00:00',
- '2011-07-19 09:00:00'], name='datetime')
- expected = expected.tz_localize('UTC').tz_convert('US/Pacific')
- df = df.set_index('label', append=True)
- tm.assert_index_equal(df.index.levels[0], expected)
- tm.assert_index_equal(df.index.levels[1],
- Index(['a', 'b'], name='label'))
- df = df.swaplevel(0, 1)
- tm.assert_index_equal(df.index.levels[0],
- Index(['a', 'b'], name='label'))
- tm.assert_index_equal(df.index.levels[1], expected)
- df = DataFrame(np.random.random(6))
- idx1 = pd.DatetimeIndex(['2011-07-19 07:00:00', '2011-07-19 08:00:00',
- '2011-07-19 09:00:00', '2011-07-19 07:00:00',
- '2011-07-19 08:00:00', '2011-07-19 09:00:00'],
- tz='US/Eastern')
- idx2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-01 09:00',
- '2012-04-01 09:00', '2012-04-02 09:00',
- '2012-04-02 09:00', '2012-04-02 09:00'],
- tz='US/Eastern')
- idx3 = pd.date_range('2011-01-01 09:00', periods=6, tz='Asia/Tokyo')
- df = df.set_index(idx1)
- df = df.set_index(idx2, append=True)
- df = df.set_index(idx3, append=True)
- expected1 = pd.DatetimeIndex(['2011-07-19 07:00:00',
- '2011-07-19 08:00:00',
- '2011-07-19 09:00:00'], tz='US/Eastern')
- expected2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-02 09:00'],
- tz='US/Eastern')
- tm.assert_index_equal(df.index.levels[0], expected1)
- tm.assert_index_equal(df.index.levels[1], expected2)
- tm.assert_index_equal(df.index.levels[2], idx3)
- # GH 7092
- tm.assert_index_equal(df.index.get_level_values(0), idx1)
- tm.assert_index_equal(df.index.get_level_values(1), idx2)
- tm.assert_index_equal(df.index.get_level_values(2), idx3)
- def test_reset_index_datetime(self):
- # GH 3950
- for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern']:
- idx1 = pd.date_range('1/1/2011', periods=5, freq='D', tz=tz,
- name='idx1')
- idx2 = Index(range(5), name='idx2', dtype='int64')
- idx = MultiIndex.from_arrays([idx1, idx2])
- df = DataFrame(
- {'a': np.arange(5, dtype='int64'),
- 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)
- expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
- datetime.datetime(2011, 1, 2),
- datetime.datetime(2011, 1, 3),
- datetime.datetime(2011, 1, 4),
- datetime.datetime(2011, 1, 5)],
- 'idx2': np.arange(5, dtype='int64'),
- 'a': np.arange(5, dtype='int64'),
- 'b': ['A', 'B', 'C', 'D', 'E']},
- columns=['idx1', 'idx2', 'a', 'b'])
- expected['idx1'] = expected['idx1'].apply(
- lambda d: Timestamp(d, tz=tz))
- tm.assert_frame_equal(df.reset_index(), expected)
- idx3 = pd.date_range('1/1/2012', periods=5, freq='MS',
- tz='Europe/Paris', name='idx3')
- idx = MultiIndex.from_arrays([idx1, idx2, idx3])
- df = DataFrame(
- {'a': np.arange(5, dtype='int64'),
- 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)
- expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
- datetime.datetime(2011, 1, 2),
- datetime.datetime(2011, 1, 3),
- datetime.datetime(2011, 1, 4),
- datetime.datetime(2011, 1, 5)],
- 'idx2': np.arange(5, dtype='int64'),
- 'idx3': [datetime.datetime(2012, 1, 1),
- datetime.datetime(2012, 2, 1),
- datetime.datetime(2012, 3, 1),
- datetime.datetime(2012, 4, 1),
- datetime.datetime(2012, 5, 1)],
- 'a': np.arange(5, dtype='int64'),
- 'b': ['A', 'B', 'C', 'D', 'E']},
- columns=['idx1', 'idx2', 'idx3', 'a', 'b'])
- expected['idx1'] = expected['idx1'].apply(
- lambda d: Timestamp(d, tz=tz))
- expected['idx3'] = expected['idx3'].apply(
- lambda d: Timestamp(d, tz='Europe/Paris'))
- tm.assert_frame_equal(df.reset_index(), expected)
- # GH 7793
- idx = MultiIndex.from_product([['a', 'b'], pd.date_range(
- '20130101', periods=3, tz=tz)])
- df = DataFrame(
- np.arange(6, dtype='int64').reshape(
- 6, 1), columns=['a'], index=idx)
- expected = DataFrame({'level_0': 'a a a b b b'.split(),
- 'level_1': [
- datetime.datetime(2013, 1, 1),
- datetime.datetime(2013, 1, 2),
- datetime.datetime(2013, 1, 3)] * 2,
- 'a': np.arange(6, dtype='int64')},
- columns=['level_0', 'level_1', 'a'])
- expected['level_1'] = expected['level_1'].apply(
- lambda d: Timestamp(d, freq='D', tz=tz))
- tm.assert_frame_equal(df.reset_index(), expected)
- def test_reset_index_period(self):
- # GH 7746
- idx = MultiIndex.from_product(
- [pd.period_range('20130101', periods=3, freq='M'), list('abc')],
- names=['month', 'feature'])
- df = DataFrame(np.arange(9, dtype='int64').reshape(-1, 1),
- index=idx, columns=['a'])
- expected = DataFrame({
- 'month': ([pd.Period('2013-01', freq='M')] * 3 +
- [pd.Period('2013-02', freq='M')] * 3 +
- [pd.Period('2013-03', freq='M')] * 3),
- 'feature': ['a', 'b', 'c'] * 3,
- 'a': np.arange(9, dtype='int64')
- }, columns=['month', 'feature', 'a'])
- tm.assert_frame_equal(df.reset_index(), expected)
- def test_reset_index_multiindex_columns(self):
- levels = [['A', ''], ['B', 'b']]
- df = DataFrame([[0, 2], [1, 3]],
- columns=MultiIndex.from_tuples(levels))
- result = df[['B']].rename_axis('A').reset_index()
- tm.assert_frame_equal(result, df)
- # gh-16120: already existing column
- with pytest.raises(ValueError,
- match=(r"cannot insert \('A', ''\), "
- "already exists")):
- df.rename_axis('A').reset_index()
- # gh-16164: multiindex (tuple) full key
- result = df.set_index([('A', '')]).reset_index()
- tm.assert_frame_equal(result, df)
- # with additional (unnamed) index level
- idx_col = DataFrame([[0], [1]],
- columns=MultiIndex.from_tuples([('level_0', '')]))
- expected = pd.concat([idx_col, df[[('B', 'b'), ('A', '')]]], axis=1)
- result = df.set_index([('B', 'b')], append=True).reset_index()
- tm.assert_frame_equal(result, expected)
- # with index name which is a too long tuple...
- with pytest.raises(ValueError,
- match=("Item must have length equal "
- "to number of levels.")):
- df.rename_axis([('C', 'c', 'i')]).reset_index()
- # or too short...
- levels = [['A', 'a', ''], ['B', 'b', 'i']]
- df2 = DataFrame([[0, 2], [1, 3]],
- columns=MultiIndex.from_tuples(levels))
- idx_col = DataFrame([[0], [1]],
- columns=MultiIndex.from_tuples([('C', 'c', 'ii')]))
- expected = pd.concat([idx_col, df2], axis=1)
- result = df2.rename_axis([('C', 'c')]).reset_index(col_fill='ii')
- tm.assert_frame_equal(result, expected)
- # ... which is incompatible with col_fill=None
- with pytest.raises(ValueError,
- match=("col_fill=None is incompatible with "
- r"incomplete column name \('C', 'c'\)")):
- df2.rename_axis([('C', 'c')]).reset_index(col_fill=None)
- # with col_level != 0
- result = df2.rename_axis([('c', 'ii')]).reset_index(col_level=1,
- col_fill='C')
- tm.assert_frame_equal(result, expected)
- def test_set_index_period(self):
- # GH 6631
- df = DataFrame(np.random.random(6))
- idx1 = pd.period_range('2011-01-01', periods=3, freq='M')
- idx1 = idx1.append(idx1)
- idx2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')
- idx2 = idx2.append(idx2).append(idx2)
- idx3 = pd.period_range('2005', periods=6, freq='A')
- df = df.set_index(idx1)
- df = df.set_index(idx2, append=True)
- df = df.set_index(idx3, append=True)
- expected1 = pd.period_range('2011-01-01', periods=3, freq='M')
- expected2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')
- tm.assert_index_equal(df.index.levels[0], expected1)
- tm.assert_index_equal(df.index.levels[1], expected2)
- tm.assert_index_equal(df.index.levels[2], idx3)
- tm.assert_index_equal(df.index.get_level_values(0), idx1)
- tm.assert_index_equal(df.index.get_level_values(1), idx2)
- tm.assert_index_equal(df.index.get_level_values(2), idx3)
- def test_repeat(self):
- # GH 9361
- # fixed by # GH 7891
- m_idx = MultiIndex.from_tuples([(1, 2), (3, 4), (5, 6), (7, 8)])
- data = ['a', 'b', 'c', 'd']
- m_df = Series(data, index=m_idx)
- assert m_df.repeat(3).shape == (3 * len(data), )
- class TestSorted(Base):
- """ everything you wanted to test about sorting """
- def test_sort_index_preserve_levels(self):
- result = self.frame.sort_index()
- assert result.index.names == self.frame.index.names
- def test_sorting_repr_8017(self):
- np.random.seed(0)
- data = np.random.randn(3, 4)
- for gen, extra in [([1., 3., 2., 5.], 4.), ([1, 3, 2, 5], 4),
- ([Timestamp('20130101'), Timestamp('20130103'),
- Timestamp('20130102'), Timestamp('20130105')],
- Timestamp('20130104')),
- (['1one', '3one', '2one', '5one'], '4one')]:
- columns = MultiIndex.from_tuples([('red', i) for i in gen])
- df = DataFrame(data, index=list('def'), columns=columns)
- df2 = pd.concat([df,
- DataFrame('world', index=list('def'),
- columns=MultiIndex.from_tuples(
- [('red', extra)]))], axis=1)
- # check that the repr is good
- # make sure that we have a correct sparsified repr
- # e.g. only 1 header of read
- assert str(df2).splitlines()[0].split() == ['red']
- # GH 8017
- # sorting fails after columns added
- # construct single-dtype then sort
- result = df.copy().sort_index(axis=1)
- expected = df.iloc[:, [0, 2, 1, 3]]
- tm.assert_frame_equal(result, expected)
- result = df2.sort_index(axis=1)
- expected = df2.iloc[:, [0, 2, 1, 4, 3]]
- tm.assert_frame_equal(result, expected)
- # setitem then sort
- result = df.copy()
- result[('red', extra)] = 'world'
- result = result.sort_index(axis=1)
- tm.assert_frame_equal(result, expected)
- def test_sort_index_level(self):
- df = self.frame.copy()
- df.index = np.arange(len(df))
- # axis=1
- # series
- a_sorted = self.frame['A'].sort_index(level=0)
- # preserve names
- assert a_sorted.index.names == self.frame.index.names
- # inplace
- rs = self.frame.copy()
- rs.sort_index(level=0, inplace=True)
- tm.assert_frame_equal(rs, self.frame.sort_index(level=0))
- def test_sort_index_level_large_cardinality(self):
- # #2684 (int64)
- index = MultiIndex.from_arrays([np.arange(4000)] * 3)
- df = DataFrame(np.random.randn(4000), index=index, dtype=np.int64)
- # it works!
- result = df.sort_index(level=0)
- assert result.index.lexsort_depth == 3
- # #2684 (int32)
- index = MultiIndex.from_arrays([np.arange(4000)] * 3)
- df = DataFrame(np.random.randn(4000), index=index, dtype=np.int32)
- # it works!
- result = df.sort_index(level=0)
- assert (result.dtypes.values == df.dtypes.values).all()
- assert result.index.lexsort_depth == 3
- def test_sort_index_level_by_name(self):
- self.frame.index.names = ['first', 'second']
- result = self.frame.sort_index(level='second')
- expected = self.frame.sort_index(level=1)
- tm.assert_frame_equal(result, expected)
- def test_sort_index_level_mixed(self):
- sorted_before = self.frame.sort_index(level=1)
- df = self.frame.copy()
- df['foo'] = 'bar'
- sorted_after = df.sort_index(level=1)
- tm.assert_frame_equal(sorted_before,
- sorted_after.drop(['foo'], axis=1))
- dft = self.frame.T
- sorted_before = dft.sort_index(level=1, axis=1)
- dft['foo', 'three'] = 'bar'
- sorted_after = dft.sort_index(level=1, axis=1)
- tm.assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
- sorted_after.drop([('foo', 'three')], axis=1))
- def test_is_lexsorted(self):
- levels = [[0, 1], [0, 1, 2]]
- index = MultiIndex(levels=levels,
- codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
- assert index.is_lexsorted()
- index = MultiIndex(levels=levels,
- codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
- assert not index.is_lexsorted()
- index = MultiIndex(levels=levels,
- codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
- assert not index.is_lexsorted()
- assert index.lexsort_depth == 0
- def test_sort_index_and_reconstruction(self):
- # 15622
- # lexsortedness should be identical
- # across MultiIndex consruction methods
- df = DataFrame([[1, 1], [2, 2]], index=list('ab'))
- expected = DataFrame([[1, 1], [2, 2], [1, 1], [2, 2]],
- index=MultiIndex.from_tuples([(0.5, 'a'),
- (0.5, 'b'),
- (0.8, 'a'),
- (0.8, 'b')]))
- assert expected.index.is_lexsorted()
- result = DataFrame(
- [[1, 1], [2, 2], [1, 1], [2, 2]],
- index=MultiIndex.from_product([[0.5, 0.8], list('ab')]))
- result = result.sort_index()
- assert result.index.is_lexsorted()
- assert result.index.is_monotonic
- tm.assert_frame_equal(result, expected)
- result = DataFrame(
- [[1, 1], [2, 2], [1, 1], [2, 2]],
- index=MultiIndex(levels=[[0.5, 0.8], ['a', 'b']],
- codes=[[0, 0, 1, 1], [0, 1, 0, 1]]))
- result = result.sort_index()
- assert result.index.is_lexsorted()
- tm.assert_frame_equal(result, expected)
- concatted = pd.concat([df, df], keys=[0.8, 0.5])
- result = concatted.sort_index()
- assert result.index.is_lexsorted()
- assert result.index.is_monotonic
- tm.assert_frame_equal(result, expected)
- # 14015
- df = DataFrame([[1, 2], [6, 7]],
- columns=MultiIndex.from_tuples(
- [(0, '20160811 12:00:00'),
- (0, '20160809 12:00:00')],
- names=['l1', 'Date']))
- df.columns.set_levels(pd.to_datetime(df.columns.levels[1]),
- level=1,
- inplace=True)
- assert not df.columns.is_lexsorted()
- assert not df.columns.is_monotonic
- result = df.sort_index(axis=1)
- assert result.columns.is_lexsorted()
- assert result.columns.is_monotonic
- result = df.sort_index(axis=1, level=1)
- assert result.columns.is_lexsorted()
- assert result.columns.is_monotonic
- def test_sort_index_and_reconstruction_doc_example(self):
- # doc example
- df = DataFrame({'value': [1, 2, 3, 4]},
- index=MultiIndex(
- levels=[['a', 'b'], ['bb', 'aa']],
- codes=[[0, 0, 1, 1], [0, 1, 0, 1]]))
- assert df.index.is_lexsorted()
- assert not df.index.is_monotonic
- # sort it
- expected = DataFrame({'value': [2, 1, 4, 3]},
- index=MultiIndex(
- levels=[['a', 'b'], ['aa', 'bb']],
- codes=[[0, 0, 1, 1], [0, 1, 0, 1]]))
- result = df.sort_index()
- assert result.index.is_lexsorted()
- assert result.index.is_monotonic
- tm.assert_frame_equal(result, expected)
- # reconstruct
- result = df.sort_index().copy()
- result.index = result.index._sort_levels_monotonic()
- assert result.index.is_lexsorted()
- assert result.index.is_monotonic
- tm.assert_frame_equal(result, expected)
- def test_sort_index_reorder_on_ops(self):
- # 15687
- df = DataFrame(
- np.random.randn(8, 2),
- index=MultiIndex.from_product(
- [['a', 'b'], ['big', 'small'], ['red', 'blu']],
- names=['letter', 'size', 'color']),
- columns=['near', 'far'])
- df = df.sort_index()
- def my_func(group):
- group.index = ['newz', 'newa']
- return group
- result = df.groupby(level=['letter', 'size']).apply(
- my_func).sort_index()
- expected = MultiIndex.from_product(
- [['a', 'b'], ['big', 'small'], ['newa', 'newz']],
- names=['letter', 'size', None])
- tm.assert_index_equal(result.index, expected)
- def test_sort_non_lexsorted(self):
- # degenerate case where we sort but don't
- # have a satisfying result :<
- # GH 15797
- idx = MultiIndex([['A', 'B', 'C'],
- ['c', 'b', 'a']],
- [[0, 1, 2, 0, 1, 2],
- [0, 2, 1, 1, 0, 2]])
- df = DataFrame({'col': range(len(idx))},
- index=idx,
- dtype='int64')
- assert df.index.is_lexsorted() is False
- assert df.index.is_monotonic is False
- sorted = df.sort_index()
- assert sorted.index.is_lexsorted() is True
- assert sorted.index.is_monotonic is True
- expected = DataFrame(
- {'col': [1, 4, 5, 2]},
- index=MultiIndex.from_tuples([('B', 'a'), ('B', 'c'),
- ('C', 'a'), ('C', 'b')]),
- dtype='int64')
- result = sorted.loc[pd.IndexSlice['B':'C', 'a':'c'], :]
- tm.assert_frame_equal(result, expected)
- def test_sort_index_nan(self):
- # GH 14784
- # incorrect sorting w.r.t. nans
- tuples = [[12, 13], [np.nan, np.nan], [np.nan, 3], [1, 2]]
- mi = MultiIndex.from_tuples(tuples)
- df = DataFrame(np.arange(16).reshape(4, 4),
- index=mi, columns=list('ABCD'))
- s = Series(np.arange(4), index=mi)
- df2 = DataFrame({
- 'date': pd.to_datetime([
- '20121002', '20121007', '20130130', '20130202', '20130305',
- '20121002', '20121207', '20130130', '20130202', '20130305',
- '20130202', '20130305'
- ]),
- 'user_id': [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
- 'whole_cost': [1790, np.nan, 280, 259, np.nan, 623, 90, 312,
- np.nan, 301, 359, 801],
- 'cost': [12, 15, 10, 24, 39, 1, 0, np.nan, 45, 34, 1, 12]
- }).set_index(['date', 'user_id'])
- # sorting frame, default nan position is last
- result = df.sort_index()
- expected = df.iloc[[3, 0, 2, 1], :]
- tm.assert_frame_equal(result, expected)
- # sorting frame, nan position last
- result = df.sort_index(na_position='last')
- expected = df.iloc[[3, 0, 2, 1], :]
- tm.assert_frame_equal(result, expected)
- # sorting frame, nan position first
- result = df.sort_index(na_position='first')
- expected = df.iloc[[1, 2, 3, 0], :]
- tm.assert_frame_equal(result, expected)
- # sorting frame with removed rows
- result = df2.dropna().sort_index()
- expected = df2.sort_index().dropna()
- tm.assert_frame_equal(result, expected)
- # sorting series, default nan position is last
- result = s.sort_index()
- expected = s.iloc[[3, 0, 2, 1]]
- tm.assert_series_equal(result, expected)
- # sorting series, nan position last
- result = s.sort_index(na_position='last')
- expected = s.iloc[[3, 0, 2, 1]]
- tm.assert_series_equal(result, expected)
- # sorting series, nan position first
- result = s.sort_index(na_position='first')
- expected = s.iloc[[1, 2, 3, 0]]
- tm.assert_series_equal(result, expected)
- def test_sort_ascending_list(self):
- # GH: 16934
- # Set up a Series with a three level MultiIndex
- arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
- ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'],
- [4, 3, 2, 1, 4, 3, 2, 1]]
- tuples = lzip(*arrays)
- mi = MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
- s = Series(range(8), index=mi)
- # Sort with boolean ascending
- result = s.sort_index(level=['third', 'first'], ascending=False)
- expected = s.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]
- tm.assert_series_equal(result, expected)
- # Sort with list of boolean ascending
- result = s.sort_index(level=['third', 'first'],
- ascending=[False, True])
- expected = s.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
- tm.assert_series_equal(result, expected)